Towards the Next Generation of Mobile Recommender Systems
Hui Xiong
Associate Professor
Rutgers University
Abstract :
Recommender systems aim to identify content of interest from overloaded
information by exploiting the opinions of a community of users. Developing
personalized recommender systems in mobile and pervasive environments is
more challenging than developing recommender systems from traditional
domains due to the complexity of spatial data, the unclear roles of
context-aware information, and the increasing availability of
environment-sensing capabilities. In this talk, we introduce the unique
features that distinguish pervasive personalized recommendation systems from
classic recommendation systems. An examination of major research needs in
pervasive personalized recommendation research reveals some new
opportunities for personalized recommendation in mobile and pervasive
applications.
Short-Biography:
Dr. Hui Xiong received his Ph.D. from the University of Minnesota and the B.
E degree from the University of Science and Technology of China (USTC). He
is currently an Associate Professor at Rutgers University, where he received
a two-year early promotion/tenure (2009), the Rutgers University Board of
Trustees Research Fellowship for Scholarly Excellence (2009), an IBM ESA
Innovation Award (2008), the Junior Faculty Teaching Excellence Award (2007)
and the Junior Faculty Research Award (2008) at the Rutgers Business School.
His general area of research is data and knowledge engineering, with a focus
on developing effective and efficient data analysis techniques for emerging
data intensive business applications. He is an Associate Editor of the
Knowledge and Information Systems journal. He has served regularly in the
organization committees and the program committees of a number of
international conferences and workshops. More detailed information is
available at http://datamining.rutgers.edu.